Genetic programming in data mining for drug discovery

Genetic programming (GP) is used to extract from rat oral bioavailability (OB) measurements simple, interpretable and predictive QSAR models which both generalise to rats and to marketed drugs in humans. Receiver Operating Characteristics (ROC) curves for the binary classier produced by machine learning show no statistical dierence between rats (albeit without known clearance dierences) and man. Thus evolutionary computing oers the prospect of in silico ADME screening, e.g. for \virtual" chemicals, for pharmaceutical drug discovery

RANCANG BANGUN GAME INTERAKTIF BERBANTUAN MODEL DISCOVERY LEARNING UNTUK MENINGKATKAN PEMAHAMAN SISWA PADA MATA PELAJARAN PEMROGRAMAN DASAR

RANCANG BANGUN GAME INTERAKTIF BERBANTUAN MODEL DISCOVERY LEARNING UNTUK MENINGKATKAN PEMAHAMAN SISWA PADA MATA PELAJARAN PEMROGRAMAN DASAR Hisyam Yusyaq, 1400494, [email protected] ABSTRAK Penelitian ini dilakukan untuk mengetahui bangaimana penerapan game interaktif berbantuan model discovery learning dapat meningkatkan pemahaman pada mata pelajaran Pemrograman Dasar di SMK Mahaputra Cerdas Utama. Tujuan dari penelitian ini adalah 1) Bahwa penerapan game interaktif berbantuan model discovery learning akan meningkatkan pemahaman pada mata pelajaran Pemrograman Dasar, 2) Bahwa pengaruh penerapan game interaktif berbantuan model discovery learning akan meningkatkan pemahaman pada mata pelajaran Pemrograman Dasar, 3) Bahwa respon siswa terhadap penerapan game interaktif berbantuan model discovery learning akan meningkatkan pemahaman pada mata pelajaran Pemrograman Dasar. Metode yang digunakan dalam penelitian ini adalah metode quasi experimental dengan desain nonequivalent control group design yang menggunakan 2 kelas sebagai objek penelitian. Banyaknya sampel yang digunakan adalah 46 orang yang diambil dari siswa kelas X RPL 1 sebagai kelas ekseprimen dan X RPL 2 sebagai kelas kontrol. Berdasarkan hasil penelitian diketahui bahwa peningkatan yang didapatkan oleh kelas kontrol maupun kelas experimen kategori sedang. Hal ini dibuktikan dengan hasil uji gain yang menunjukan nilai gain pada kelas kontrol sebesar 0,39 sedangkan pada kelas experimen sebesar 0,56. Hal tersebut menunjukan bahwa penerapan game interaktif berbantuan model discovery learning dapat meningkatkan pemahaman pada mata pelajaran Pemrograman Dasar lebih tinggi yaitu sebesar 56% dibanding tanpa menggunakan multimedia yaitu sebesar 39%. Selain itu, multimedia ini juga berpengaruh tinggi terhadap minat belajar siswa. Sedangkan hasil penilaian berupa angket siswa terhadap multimedia menunjukan pada kategori sangat baik dengan memperoleh rata-rata nilai sebesar 87,2%. Kata Kunci: Discovery learning, Pemrograman Dasar, Game, pemahaman.   DESIGN AND DEVELOPMENT OF INTERACTIVE GAMES ASSISTED BY DISCOVERY LEARNING MODEL TO IMPROVE STUDENT UNDERSTANDING IN BASIC PROGRAMMING LESSONS Hisyam Yusyaq, 1400494, [email protected] ABSTRACT This research was conducted to find out how the application of interactive games assisted by discovery learning models can improve understanding in the Basic Programming subjects at the Mahaputra Cerdas Utama Vocational School. The purpose of this study is 1) That the application of interactive games assisted by discovery learning models will increase understanding in Basic Programming subjects, 2) That the influence of the application of interactive games assisted by discovery learning models will increase understanding in Basic Programming subjects, 3) That students' responses to the application of interactive games assisted by discovery learning models will enhance understanding in Basic Programming subjects. The method used in this study is a quasi experimental method with a nonequivalent control group design that uses 2 classes as the object of research. The number of samples used was 46 people taken from class X RPL 1 students as the executive class and X RPL 2 as the control class. Based on the results of the study it was found that the increase obtained by the control class and the experimental class was in the medium category. This is evidenced by the results of the gain test which shows the gain value in the control class is 0.39 while the experimental class is 0.56. This shows that the application of interactive games assisted by discovery learning models can improve understanding in the Basic Programming subjects higher, which is equal to 56% compared to without using multimedia that is equal to 39%. In addition, multimedia also has a high influence on student learning interest. While the results of the assessment in the form of student questionnaires on multimedia showed a very good category with an average score of 87.2% Key words: Discovery learning, Basic Programming, Games, understanding

Combining Programming-by-Example with Transformation Discovery from large Databases

Data transformation discovery is one of the most tedious tasks in data preparation. In particular, the generation of transformation programs for semantic transformations is tricky because additional sources for look-up operations are necessary. Current systems for semantic transformation discovery face two major problems: either they follow a program synthesis approach that only scales to a small set of input tables, or they rely on extraction of transformation functions from large corpora, which requires the identification of exact transformations in those resources and is prone to noisy data. In this paper, we try to combine approaches to benefit from large corpora and the sophistication of program synthesis. To do so, we devise a retrieval and pruning strategy ensemble that extracts the most relevant tables for a given transformation task. The extracted resources can then be processed by a program synthesis engine to generate more accurate transformation results than state-of-the-art

jsdp: a Java Stochastic Dynamic Programming Library

Stochastic Programming is a framework for modelling and solving problems of decision making under uncertainty. Stochastic Dynamic Programming is a branch of Stochastic Programming that takes a "functional equation" approach to the discovery of optimal policies. By leveraging constructs - lambda expressions, functional interfaces, collections and aggregate operators - implemented in Java to operationalise the MapReduce framework, jsdp provides a general purpose library for modelling and solving Stochastic Dynamic Programs.Comment: 8 page